Plot1

Plot1 Notes

These figures highlighted in plot 1 are visualizations of the mean accuracy of participants sepparately for a expectation cue and stimulus stream consistancy conditions during a cue biased multisampling judgement task. The cue indicated the correct category of the stream 70% of the time and elements in the stream were categorically assigned to one of 2 axis groups. Participants were instructed to correctly identify the overall cateory of the stream. The Invalid category indicates the trials in which the expectation cue presented before the stimulus stream was not consistant with its correct identity. The Valid category indicates trials in which the expectation cue presented before the stream was consistat with its overall identity.

1st Iteration: Constructed using geom_bar() with error bars visualized through geom_errorbar().

2nd Iteration: Constructed using geom_jitter() to replace geom_bar with participant mean scores around consistancy conditions. Problems correctly segmenting a bar indicating the mean value for each group with geom_hline().

3rd Iteration: Correctly implementing segmented group mean bars using annotate() to manually place and lable them.

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Plot2

Plot2 Notes

The figures highlighted in plot 2 are visualizations of the correlation between individual mean accuracy values of Invalid cue trials and Valid cue trials. The plot indicates individuals reling on the expectation cue tend to have higher accuracy during valid trials and lower accuracy during invalid trials (and vice versa).

1st Iteration: Constructed using plot() and indexing valid/invalid rows of dataset.

2nd Iteration: Constructed using ggplot(), geom_point(), and geom_smooth(). Initially included very wrong effect size value.

3rd Iteration: Implemented an animated layer of the regression line using bootstrapper(). Colorblind friendly palatte also implemented alongside minor changes to figure fonts.

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Plot3

Plot3 Notes

Mock figures plotting the coefficients of the fixed effects from a generalized linear mixed model as decision weights in predicting a cardinal trial identity. Congruent indicates trial elements within trial presentation position that were consistant with the expectaion cue. Incongruent indicates elemetns specific to position that were not consistant with the expectation cue. Potential missing predictors in model may skew interpreation but congrency effect differences are stronger in position 1. As evidence accumulates, weights of each congruency group for each evidence position becomes similar.

1st Iteration: Constructed using ggplot(), geom_line, and geom_point. Used theme to manipulate plot text font and identity.

2nd Iteration: Implemented correct mapping of error bars which did not work initially due to data formatting. Potentially(?) colorblind freindly color palette used for congruency category.

3rd Iteration: Failed implimentation of stat_confidence_density() for CI visualization. Defaulted to geom_ribbon to implement hypothetical error bars connecting positions, potentially usefuly for a visualizing experiment using more element positions to track decision weights.

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